scholarly article | Q13442814 |
P819 | ADS bibcode | 2016PhRvE..93c2323N |
P356 | DOI | 10.1103/PHYSREVE.93.032323 |
P698 | PubMed publication ID | 27078382 |
P50 | author | Michael Small | Q38804353 |
P2093 | author name string | Tomomichi Nakamura | |
Toshihiro Tanizawa | |||
P2860 | cites work | Statistical mechanics of complex networks | Q21563680 |
Emergence of Scaling in Random Networks | Q27037290 | ||
Collective dynamics of 'small-world' networks | Q27861064 | ||
Hierarchical structure in financial markets | Q28109466 | ||
Superfamily phenomena and motifs of networks induced from time series. | Q37018775 | ||
Hierarchical group dynamics in pigeon flocks | Q43454470 | ||
Horizontal visibility graphs: exact results for random time series | Q44576041 | ||
Scale-free distribution of avian influenza outbreaks. | Q50867550 | ||
Testing for correlation structures in short-term variabilities with long-term trends of multivariate time series. | Q51095898 | ||
Complex network from pseudoperiodic time series: topology versus dynamics. | Q51172322 | ||
Topology and predictability of El Niño and La Niña networks. | Q51872544 | ||
Climate networks around the globe are significantly affected by El Niño. | Q55050238 | ||
The Structure and Function of Complex Networks | Q55879008 | ||
Complex network approach for recurrence analysis of time series | Q56430802 | ||
Testing for nonlinearity in time series: the method of surrogate data | Q56689091 | ||
P433 | issue | 3 | |
P407 | language of work or name | English | Q1860 |
P921 | main subject | multivariate statistics | Q1952580 |
P304 | page(s) | 032323 | |
P577 | publication date | 2016-03-28 | |
P1433 | published in | Physical Review E | Q2128181 |
P1476 | title | Constructing networks from a dynamical system perspective for multivariate nonlinear time series | |
P478 | volume | 93 |
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Q38813070 | Persistent homology of time-dependent functional networks constructed from coupled time series |
Q37618121 | Topological Characteristics of the Hong Kong Stock Market: A Test-based P-threshold Approach to Understanding Network Complexity |
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